Estimación de Modelos de Equilibrio General en Economías Dinámicas por Métodos de Monte Carlo y Cadenas de Markov

  1. Estévez, Gloria 1
  2. Infante, Saba 2
  3. Sáez, Francisco 1
  1. 1 Banco Central de Venezuela, Oficina de Investigaciones Económicas
  2. 2 Universidad de Carabobo, Centro de Análisis, Modelado y Tratamiento de Datos, y Departamento de Matemática, Facultad de Ciencias y Tecnología
Revista:
Revista de Matemática: Teoría y Aplicaciones

ISSN: 2215-3373,  2215-3373

Año de publicación: 2012

Volumen: 19

Número: 1

Páginas: 7-36

Tipo: Artículo

DOI: 10.15517/RMTA.V19I1.2102 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Revista de Matemática: Teoría y Aplicaciones

Objetivos de desarrollo sostenible

Resumen

This paper describes a general procedure to do Bayesian inference based on the likelihood evaluation of the stochastic general equilibrium models (MEGE) through Markov Chain Monte Carlo methods (MCMC). The proposed methodology involves log linearizing the model, transformed into state space form, then use the Kalman filter to evaluate the likelihood function and finally apply the Metropolis Hastings algorithm to estimate the posterior distribution parameters. Technique is illustrated using the stochastic growth of basic model, considering quarterly data on the Venezuelan economy between the first quarter of (1984) through the third quarter of (2004). The empirical analysis made allows us to conclude that the algorithms used to estimate the model parameters work efficiently and low computational cost, the estimates obtained are consistent, that is, estimates of the predictions adequately reflect the behavior of the product, employment, consumption and investment per capita in the country. The graphs of the estimated histograms show bimodal and skewed distributions.

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